The Transfer Challenge to Expertise

Consider this relatively commonsensical view of problem solving:

“We absorb information in some common generic way and then apply our individual talents to using that information to solve problems.”

As straightforward and intuitive as this description sounds, it contains a counter-productive assumption about knowledge, confusing it with information. We tend to assume that the things we experience are experienced in the same way by other people. We might all look at the same world, but we make different observations and draw different conclusions from it. The knowledge base we each build over our lifetime is not just a straightforward result of the information presented to us; it is also in part a result of how we organize that information, which can be very different from one person to another. This is the foundation of the expertise model.

Organization Matters

It is not enough to have the right information to solve a problem, the information must also be organized in a way that lets us think about it in the right way.

Expertise is a key factor in effective problem solving because it permits us to organize information about a domain, recognize patterns in that domain, apply domain knowledge to new kinds of problems, and incorporate new information about that domain.

The expertise model gives us several key insights into the problem solving process that the commonsensical view above misses:

It tells us that the way information is organized is critical to how that information can be used for problem solving. Many problem solving principles will deal with how information is best organized to solve problems.
It tells us that in certain key areas, deep accurate understanding is important and not just superficial familiarity. Both intuitive decision making and more formal methods rely on deep accurate understanding acquired from experience.
It tells us that a great deal of deliberate practice with good feedback is needed to acquire deep understanding.

It tells us that more skillful problem solving emphasizes principles, while less skillful problem solving relies on procedures.
In spite of the tremendous power of the expertise model, it has its own limitations as well. Expertise lets us detect meaningful patterns of information in particular domains because of the way we organize our own knowledge.

The organization of domain-specific knowledge in our mind has important implications. It means that experts have differently organized knowledge for thinking in different domains. The fact that the expertise model is so thoroughly domain-specific forces us to now confront the most serious challenge of all to the expertise model: the challenge of transfer. If it takes so much deliberate practice to become good in a given domain, how does anyone manage to become good at more than a very narrow range of activities? How do our narrowly cultivated abilities support other activities? Or do we have important abilities that are not domain-specific as well? What does it take to apply our hard-won expertise to problems different than the ones we specifically practiced for?

The Failure of Mental Exercise

At one time, it was widely believed that people could develop their mind by doing mental work such as solving logic puzzles, learning mathematics, reading classics of literature, and learning to speak Latin. A long history of sometimes large scale research on this approach to mental ability revealed it to apparently have very little promise.[1] Literacy in general, while valuable for its own sake, simply does not have much effect on other thinking abilities.[2] Our ability to solve puzzles doesn’t tend to generalize very well unless the training specifically teaches the underlying patterns and provides us with a way of remembering them.[3] We don’t automatically apply the lessons of solving one problem to solving a structurally similar but different problem. The different appearance of problems tends to throw us off. When we learn strategies for solving problems, we tend to learn them in a way that is tied to the specific kinds of problems that we used for learning them. Expertise, the research confirms, tends to be very context specific.

How Transfer Does Happen: Two Roads

The problem with this result is that while it seems consistent with the expertise model, it doesn’t quite make sense in terms of our everyday experience. All of us routinely do apply what we know to new kinds of problems. We aren’t equally incompetent in dealing with any sort of novel problem; our existing expertise clearly does sometimes give us an advantage in another domain. We also see negative influence of expertise when our existing abilities interfere with our attempts to perform in a similar domain. Expertise does seem to transfer between domains under some conditions. The question is what those conditions might be.

Research done in the late 1980’s and early 1990’s confirmed that transfer of ability between domains does occur consistently under certain conditions. One cognitive psychologist working with preschool children on simple tasks discovered that the 3 and 4 year olds could use lessons they learned under one set of conditions in a completely different set of conditions, but especially if they were shown how the different problems resembled each other and how the goals were similar, they were familiar with the problem areas, the examples also had rules associated that the children figured out for themselves, and if the learning took place in a social context that specifically encouraged them to spell out the principles, explanations, and justifications to use.[4]

Research such as this led to a general two-pronged theory of transfer, proposed by David Perkins and Gavriel Salomon. The theory is based on the finding that transfer sometimes takes place between similar domains, and sometimes takes place between very dissimilar domains, and that these seem to happen under different conditions.[5]

What the Perkins and Salomon theory calls “low road transfer” happens when situations appear to us to be similar according to simple perceptual cues rather than any deep structural pattern. This seems to be a matter of stimulus triggers. Specific elements in the situation help us recognize and apply skills and knowledge from our memory based on recognizing those elements from our practice. Since low road transfer is pattern-bound, it doesn’t generally lead to transfer to different situations. Practicing under a variety of different conditions however can help is gradually stretch our skills from one context to a similar one to generalize our skills further. Low road transfer is a result of the variety of conditions under which we practice rather than any specific cognitive skills or strategies aside from those specific to the domain. Low road transfer is a perceptual-memory phenomenon.

When a situation bears a superficial similarity to one we’ve trained for, we recognize stimulus patterns and our expertise is evoked via low road transfer. This is how many people manage to drive a truck reasonably well after having learned to drive a car for example. Even though the mechanics are very different, the steering, pedals, and so on are all familiar enough to trigger our learned skills for driving. That is, until we find ourselves in a situation where the fit isn’t so good between our skills and the ones that are needed.

What the Perkins and Salomon theory calls “high road transfer” seems to be a completely different matter. High road transfer involves the deliberate and mindful abstraction of principles during practice and using more general cognitive skills and strategies to apply them to completely different situations. In high road transfer, the learner actively seeks connections between different situations in which to apply the principles they’ve learned. High road transfer is a cognitive phenomenon.

We see that the similarity mechanism of transfer is limited. It only works for relatively similar situations and it works in a very automatic and unthinking way. To apply expertise to a very differently appearing situation with underlying structural similarity (such as we might need for more abstract problem solving) we need high road transfer and we need to use abilities we associate with conscious reflection. This allows us to transfer expertise from deliberate abstraction of principles to entirely different kinds of problems.

The lesson of the transfer challenge to expertise is that expertise does not automatically apply outside of its domain. We have to very deliberately either: (1) work on practicing in widening ranges of situations to facilitate generalization or (2) work on abstracting and applying general principles mindfully from our practice, or both.

Conclusion: Transfer and Expertise

We’ve seen that expertise is a very powerful model that explains in some detail how we organize tacit knowledge for recognizing patterns and solving a particular domain of problems. This appears to explain the lion’s share of differences in human abilities in problem solving. We’ve also seen that expertise can be acquired in such a way that it can be generalized to an increasingly wider range of conditions and in a way that makes it less likely to fail catastrophically under extreme conditions, making expertise a potentially very robust resource for problem solving.

We’ve also seen that the expertise model misses a small but critical aspect of problem solving; it does not tell us how people manage to deal with surprises or with domains that are characterized by surprises. The expertise research consistently shows strong dependence on specific contexts. We do not automatically generalize our skills or strategies to new kinds of problems just by acquiring deep expertise in a domain.

Novelty offers our most serious challenge to the power of expertise. The very concept of expertise implies domain-specificity, and domain-specificity implies that expertise is honed to deal effectively with a particular range of situations. Novelty, both within a domain and outside that domain, creates problems for the standard expertise model that need to be addressed.

Novel but superficially similar situations can be handled through expertise, but only if we specifically widen our practice to deal with a broader range of conditions.

Completely novel situations in other domains that don’t resemble the ones we practice for except in terms of their underlying deep structure can be handled through expertise as well, but only by deliberate attention to learning and applying general principles as well as acquiring domain expertise.

The Story So Far: Going Beyond Expertise

The expertise model tells us how we acquire useful patterns of tacit knowledge from experience through deliberate practice with good feedback. The expertise model explains how we deal effectively with the sorts of situations where we have accumulated extensive practice. Expertise thus acquired becomes part of our intuitive understanding of situations, enhancing, modifying, and extending our existing commonsense intuitions.

The expertise model also challenges us to explain how it is that we are able to deal with extreme yet realistic conditions and novel problems even though expertise tends to be very context-specific. Applying expertise to very different situations requires deliberate mindful work at abstracting principles and applying them through our capacity for reflective thinking. This kind of reflective thinking is not adequately captured by the expertise model. Either we need to expand the expertise model to handle the challenges we have identified, or else we need a more expansive concept to describe our abilities.



[1] (Thorndike & Woodworth, 1901), (Thorndike, The influence of first year Latin upoin the ability to read English, 1923)

[2] (Scribner & Cole, 1981)

[3] (Simon & Hayes, Psychological differences among problem isomorphs, 1977)

[4] (Brown, 1989)

[5] (Perkins & Salomon, 1987), (Perkins & Salomon, Teaching for transfer, 1988), (Salomon & Perkins, 1989)

The Unpredictability Challenge to Expertise

We almost universally recognize the legitimacy of experts in a number of different domains. In many academic fields such as mathematics, sciences, history, literature, and other academic areas, some people know much more and consistently perform much better than others in tests of ability. Similarly for many professional fields and various sports and games, we recognize that there are experts who outperform the majority of us.

A key finding in modern learning and human performance research has been the discovery of how expertise is acquired.[1] This discovery became possible with the advent of cognitive science, allowing us to model the human brain as an organized collection of information rather than just a collection of behavioral patterns. As we learned about the specific differences between novices and experts[2] in each field, we discovered certain general principles that apply to a very wide range of different fields.

A formidable body of this type of research has overturned the intuitive view that novices and experts differ because some people are simply more naturally talented than others. Experts seem to perform so effortlessly that we tend to attribute great natural ability to them rather than a different kind and degree of experience. Contrary to this intuition, expertise via deliberate practice is our best model so far of individual differences in ability in a wide range of activities. Expertise is a result of experience, and not just any experience, but deliberate practice where we meet challenges in that domain, are immersed in purposeful practice, gain knowledge from other people who are already good at it, have good coaches, and benefit from quality feedback for our performance.[3]

At the same time as verifying the legitimacy of expertise in many fields and establishing the central role of deliberate practice, social environment, coaching, and feedback, we have also discovered that there are some fields where our performance doesn’t benefit from these factors.

In spite of the tremendous power of the expertise model, it has its own limitations as well. Expertise lets us detect meaningful patterns of information in particular domains because of the way we organize our own knowledge. This assumes that there are meaningful patterns to detect that human beings are capable of using effectively. This is not always the case.


Where the Experts Fail

In the 1950’s, research into medical diagnosis and prognosis revealed something shocking: presumed experts didn’t seem to predict medical outcomes any better than novices. This line of research continued over time to demonstrate that prognosis and diagnosis in clinical work in medicine was often not improved by experience when experts relied upon informal gathering of data and their trained intuitions.[4]

Professional experience and presumed expertise also seem to make no difference in predicting the outcome of psychotherapy by psychologists, who it turns out also fare no better than less trained individuals.[5]

If there is a skill to predicting medical and psychotherapeutic outcomes in general, it doesn’t seem to be acquired from the standard professional training, or typically through experience with patients, and it isn’t obvious how else it might be acquired. There is perhaps good reason why some experienced doctors seem very reluctant to make predictions about outcomes, and maybe more of them should heed this lesson.

As a result of the difficulty of prediction in areas like this, novices using simple formal statistical methods have often outperformed the experts in tests in spite of the greater experience and training of the experts (or perhaps in some cases partly because of it).

This is not by any means to imply that statistical methods are always superior to expertise, even in a particular field where clinical experience has proven less than optimal. However, it does give us good reason to pause and reflect on the meaning of this finding for the expertise model. In some domains, the best we can do for prediction is provided by a simple statistical method; and the value of expertise in particular reaches a limit fairly early on in the training for those fields.

What Makes the Value of Expertise Vary So Much?

Research into the value of expertise in different domains shows it to vary[6] with:

1.the level of inference[7] required (moderate levels of inference are more conducive to using expertise than high levels of inference),
2.whether experience or training available is adequate to confer expertise,
3.whether the conditions and instruments available allow for the expression of expertise

What this tells us is that even though expertise helps us make sense of complex situations by recognizing patterns, there is also a limit to how well acquired expertise can help us make better judgments in very complex situations. The more specialized knowledge we need in a field just to understand what is going on, the more likely it is that expertise will fail us when the situation requires a great deal of challenging inference. In the most complex fields at least, it may be that intelligence can also play an important role alongside expertise.[8]

Both intelligence and expertise play some role in every field, but each is more important to some fields than others and at different points in the development and expression of ability. The relevance of intelligence in a field seems to depend to a large degree on the role that abstract reasoning plays in success in that field. The relevance of expertise is more general. The role of expertise in a field depends on how well the situation is made comprehensible to the expert through specialized tools, the quality of their training and experience, and the kinds of conditions in which they have to perform.

Even allowing for a role for intelligence in particularly difficult technical fields requiring very high inference levels, there are fields where neither expertise nor intelligence nor any combination of the two seems to predict performance any better than simple methods.

We’ve discovered that in some areas, experts perform significantly better than non-experts and consistently outperform computer models of various kinds because of their rich background of task-relevant skills and knowledge. In .other areas, simple computer models, statistical indexes, and non-experts consistently outperform experts.

Intelligence may play more of a role in ability in highly technical domains where a high level of inference is often required in addition to recognizing important patterns. Domains are apparently not all equal with regard to what it takes to be good at them.

How Surprises Can Negate Expertise

The difference has to do with the varying role of understanding the situation for solving problems in different fields, and the role that surprise plays in each field. Fields involving things that move freely and things that scale wildly rather than behaving according to standard statistical methods[9] tend to produce surprises that can’t be managed primarily by either intelligence or expertise or both. In these areas the requisite intelligence is relatively low and the practical role of expertise is relatively marginal because reasoning doesn’t help much and it is particularly difficult to get the necessary skills even if you can identify them. So in these fields, simple statistical rules can sometimes perform as well as any expert, regardless of their IQ.

For examples of fields more or less dominated by surprises think of stockbrokers, risk management advisors, clinical psychologists, counselors, psychiatrists, admissions officers, court judges, economists, financial advisors, and intelligence analysts. Think in general of all the fields where experts fare poorly compared to non-experts, where overconfidence cancels out the benefits of expertise, or where time spent in formal practice has relatively little impact on effective outcomes.

In these fields, formal domain-specific expertise and general intelligence provide relatively little advantage in producing good outcomes compared to simple algorithms, direct local observation, direct experience, practical skills, and domain general problem solving skills. Formal expertise and intelligence in these fields especially tends to produce overconfidence more than real predictive ability. It’s not impossible that there may be some real experts in these fields who fare better than others, but they are particularly difficult to identify and train with formal methods.

Not all fields are dominated by surprises regardless of intelligence and expertise. Think of fields involving things that stay put or else move within strictly defined ranges according to physical laws or arithmetic or statistical relationships. These are much better suited to intelligence and domain-specific expertise because in those fields a better understanding of identifiable patterns and potentially complex information does tend to lead to better prediction of outcomes. Think of theoretical mathematicians and physicists, astronomers, test pilots, firefighters, livestock, grain, and soil judges, accountants, chess masters, insurance analysts (who deal with Gaussian topics like mortality), competitive athletes, and surgeons. Think in general of the many fields studied by expertise researchers where deliberate formal practice yields measureable improvements in results, and where the critical skills can be identified and trained.

We Don’t Learn Well from History

Part of the problem with expertise in fields where surprise plays an important role is that we don’t learn well from history in general. One of our consistent biases is that we systematically overweight the likelihood of events that actually happened, relative to ones that didn’t happen (but could have). This means that we have a very strong predisposition to describe events that happened as if they were fated to happen that way. This also means that we tend to think of our descriptive stories as if they were also explanations, not just descriptions. The remarkable power of stories becomes a disadvantage for explanation because the narrative content tends to replace our ability to analyze cause and effect.[10]

We become experts by being exposed to similar conditions over and over again and learning from consistent patterns in our experience. When a domain is characterized by events that are relatively uncommon yet influential, our confidence in our ability to predict events in that domain tends to grow way out of proportion to our actual ability to predict or explain the course of events. Our hindsight bias (“I knew it all along”) often kicks in to replace our missing explanatory ability.[11]

The human mind is particularly well suited to remembering and making sense of events after the fact by weaving facts into a plausible narrative, and particularly poorly suited to capturing actual frequencies of events in order to use that information in other judgments. Our common sense excels at generating plausible stories for what happens, our expertise then generates trained intuitions that add to our confidence in our explanations, but in some cases does not also add to our explanatory ability. Then history leaves us with only a single chain of events to explain, the one that actually happened. We infer from all of this that we are explaining why a sequence of events took place in a particular situation, whereas we have often only described the events, not explained them.[12]

Conclusion: Surprises and Expertise

We saw in the previous section that extremes of arousal can negate some kinds of expertise, especially expertise relying on fine motor skills. We also saw that our mindset can determine whether expert performance is retained during high arousal or fails catastrophically. In addition we saw that our ability to flexibly adapt our responses to novelty in the situation is hampered by high arousal.

Now we see that novelty offers a more general and more serious kind of challenge than just our tendency to lock in to central stimuli under high arousal. When relatively uncommon events tend to be influential in a domain, the power of expertise to help us predict and explain events is severely compromised and often even negated entirely. In these cases we have a compelling natural tendency to tell plausible stories and rely on them as explanations, and additional expertise only serves to increase our overconfidence.


[1] A June 2008 review of major trends in expertise research: (Charness & Tuffiash, 2008)

[2] An expert is typically defined for research purposes as someone who consistently performs more than two standard deviations above the mean average performance on representative tasks for their domain, assuming that ability in the field can be represented by measureable tasks and that this ability is normally distributed (Ericsson & Charness, 1994). Experts defined in this way are assumed to be roughly the top 5% of the performers in a field.

[3] The body of research has been variously summarized in popular books by journalists but a far better source for reviewing the evidence directly is the edited technical article collection: The Cambridge Handbook of Expertise and Expert Performance (Ericsson, Charness, Feltovich, & Hoffman, 2006)

[4] Classic early research showing the limits of human judgment from experience was done by Paul E. Meehl. Meehl demonstrated the limits of informal aggregation of data and prognostication by presumed experts in clinical situations such as diagnosing patients and predicting medical outcomes (Meehl, 1954). An influential review of research showing the superiority of actuarial vs. clinical judgment appeared in the journal Science in 1989: (Dawes, Faust, & Meehl, 1989)

[5] (Dawes, 1994)

[6] (Westen & Weinberger, 2005)

[7] The level of inference means the amount of specialized individual knowledge needed to understand what is going on. Situations with low levels of inference are understandable by most people, those with high levels of inference are only accurately understood by experts. Even experts utilize their abilities better in situations of lower levels of inference.

[8] There is a lot of ongoing controversy about various aspects of intelligence measurement and what it can tell us, but one of the things that most theorists agree on regarding individual differences in intelligence measurements is that they seem to correspond in some sense to our capacity to handle complexity. (Neisser, et al., 1996)

[9] This was one of the main points made by Nassim Nicholas Taleb in his entertainingly and ironically sharp book exhorting the importance of epistemological humility in the face of this sort of unpredictability in important domains, The Black Swan (Taleb, 2007)

[10] For examples in technical literature making this argument more clearly, see: (Lombrozo, 2006), and (Lombrozo, 2007). The point is made even more emphatically in (Dawes R. , 1979). Formal techniques for causal analysis take the lure of stories explicitly into account by using various methods to compensate for it and force analysts to think in causal terms rather than relying on our more natural instincts for telling stories about what happened. (Gano, 2008)

[11] A good technical article introducing hindsight bias and the related idea of “creeping determinism” (what happened is what was most likely to happen) is (Fischhoff, 1982)

[12]There is a more detailed discussion of the difference between stories and explanations in the chapter History is a Fickle Teacher in (Watts, 2011, pp. 108-134)

The Limitations of Expertise

The Limitations of Expertise

Although it covers a very wide range of activities, the large body of expertise research takes place in areas where we can easily identify how good people are based on standards of performance within the field itself, and where, to put it bluntly, skill matters. What about performance in real world, where things are a lot messier, and the more skillful exponent doesn’t always come out on top?

This indeed turns out to be a very real issue. While having a certain amount of skill is always valuable, it isn’t always the case that being more skillful means that we perform even better. A little skill might be good, but more skill might not be better. How can this be true?

Consider these possibilities for why being more skillful might not make us perform better:

1. Extremes of Arousal: The general state of our nervous system in response to a situation can in turn affect the performance of our trained skills, although the reason for this is surprisingly poorly understood theoretically. Picture trying to drive a challenging obstacle course while very sleepy, anxious, or terrified. Extremes of arousal may plausibly affect expertise, and perhaps even negate large differences in expertise, although the effects would probably depend on some interaction of the type or activity and whether it was low or high arousal. And it turns out that the way we interpret the situation can be an important factor as well.

2. Transfer Failure: The situation at hand may resemble the situation we practiced for, but be different enough that our skills matter less. If I learn to drive a car and then manage to drive a truck, I’m transferring my skills. If I crash the truck because I can’t figure out how to operate the different controls properly or because the different response of vehicle confuses me, then we have transfer failure. My expertise doesn’t help me if it doesn’t transfer to the situation I’m in.

3. Domain Unpredictability: Some things seem to be intrinsically difficult to predict, so no amount of experience makes us better at predicting things in those domains. I don’t necessarily get better at predicting earthquakes by living through a few earthquakes, and I don’t necessarily get better at predicting slot machine payoffs by playing more, although I might learn other valuable lessons.

Despite the power of expertise across such a wide range of activities, it’s entirely possible that our performance may depend more on something other than expertise under some conditions. I’m going to examine these challenges to the power of expertise one at a time.

The Power of Expertise

The Power of Expertise

Who Ya Gonna Call?

Let’s say you’re working on your computer and it starts acting strangely. You get errors that don’t understand or it crashes for no apparent reason. If you aren’t sure what to do at first, where will you look for help? You might perform a web search for the symptoms to see if it’s a known problem and other people have solved it before you. You might run some diagnostic program or an antivirus scan because those are the tools you happen to have.

If you can’t fix it easily and you aren’t confident with computers you’ll probably start looking for help from another person at some point. Who? If it were me, I probably wouldn’t head down to the local college and find the top honors student or someone in the local Mensa chapter. I probably wouldn’t look for someone with great SAT scores or someone really good at Sudoku or even a master electrician. I’d look for someone with a lot of experience with computers and a proven track record fixing them. I’d look for an expert, and an expert specifically in that area, not just a smart person or an expert in a related area.

I stacked the deck a little bit with this question, because I picked a problem that is probably going to be technical in nature. That is, it seems like it will require some specialized knowledge to solve because it involves computers which are complicated devices that are a little mysterious to the average person and far less so for someone who has worked extensively with them.

It turns out, though, that my guess is pretty accurate for a wide range of fields, not just highly technical ones. Knowledge about the job turns out to be a far better predictor of performance than how high our IQ is or any other general disposition, not just in certain kinds of jobs but across a wide range from complex technical work to manual labor.[1] Just as I’d rather have a computer expert help me rather than my friend with an astronomical IQ, in most cases I’d prefer someone who has job experience rather than someone very smart but inexperienced. And I can point to research evidence that supports my preference.

Seeing Differently vs. Seeing More

Even in many areas where we would tend to expect pure reasoning ability to play a large role, it turns out that on average experience tends to win out consistently over any more general ability or measurement we have come up with.

The research that inspired the modern study of expertise began with the game of chess. Think about chess for just a moment. Chess is an activity with a small number of relatively simple rules. Yes chess has the reputation for being a difficult game. But that’s not because chess is hard to play. Nearly anyone can learn the game. It’s because we soon discover that differences in individual ability are immense.

The difference between someone who plays chess for fun who doesn’t study the game seriously, and an average tournament player, is like night and day. It doesn’t seem like much of a competition most of the time. The difference between an average tournament player and a strong one is just as large, which is why there is a rating system.

Ratings allow people of similar ability to play relatively evenly, or to estimate handicaps as they do in golf. The difference between a strong player and a master is similarly imposing as is that between the master and a grandmaster, and between the average grandmaster and a world champion.

How can a game with a handful of simple rules end up with people playing at such astronomical differences in ability? This was the question that intrigued early researchers trying to figure out how people solve problems. The obvious answer is that the stronger players must be seeing more on the board. But what are they seeing differently?

When most of us look at the chess board we see a collection of pieces in different places that are allowed to move in particular ways. We know what we have to do to win; we have to trap the king. We also know some ways to accomplish that. For example we can capture the opponent’s pieces so we have a bigger army, and we can harass the opponent’s pieces so that they are forced into a less defensible position, allowing us to attack the king. Everyone who plays the game, even for fun, knows these things. Still most of us pretty much have to guess at how to get from some arbitrary position to that result.

If I move here, I’ll attack this piece, but how do I know that my opponent doesn’t have some better move in response that is even stronger? More insidiously, is that move by my opponent actually setting up a surprise for me later? If so, what are my options? These kinds of considerations quickly lead to the very intuitive notion that being better at chess is really about calculation, about being able to imagine a lot of different moves, and what might happen if we made them, and keeping track of all that imagining. The better player must be seeing more moves on the board, figuring out what the options are more accurately, and then predicting the outcome.

This is indeed how early chess software played the game well. It looked at the possible moves, looked at the possible responses to each move, evaluated the resulting positions, and chose the move that seemed to give the best outcome based on what the opponent was able to do. The trouble was that trying to do this more than a couple of moves ahead turned out to be a very demanding calculation. More demanding than even the most powerful computers could handle. Researchers were curious as to whether seeing more moves in their mind is really what good players were doing.

Maybe the human brain is really that much more powerful at calculation than we thought. Or maybe the brain is doing something else entirely?

In a pioneering study of chess players in the 1940’s[2] a Dutch psychologist found the surprising answer. I say his work was pioneering not just because it was early but because it led to entire fields of research based upon it and validating his basic findings. The most compelling and surprising findings:

...Weaker players examined the same number of moves as stronger players, and equally thoroughly (!)

...Stronger players could recognize an actual game position far better than weaker players.

...Stronger players were just as bad as weaker players at recognizing an arbitrary configuration of pieces.

This may not seem so earthshattering at first, but think about the implications. Experts at chess consistently beat weaker players, but without examining more moves and without examining the outcomes of those moves more thoroughly. They aren’t “looking ahead more” and they aren’t “reasoning better” and they aren’t even remembering more in general. They do remember more about chess in a sense but not because they have a better memory. And looking ahead is important, but not by keeping track of moves. Their ability is a result of their mind being better trained to remember chess configurations in particular and to use that knowledge quickly and efficiently to evaluate moves.

So what are chess experts seeing that the rest of us aren’t? They aren’t seeing more moves ahead, they are seeing the board in terms of chess configurations instead of seeing it in terms of individual pieces. Their mind has been trained to see meaningful configurations of pieces instead of individual moves. They are not seeing more per se, they are seeing differently. They are seeing in terms of larger and more meaningful groupings. Experts with extended experience acquire a larger number of more complex patterns and use these new patterns to store knowledge about which actions should be taken in similar situations.[3]

The result is profound. We have a game where a few simple rules results in an incalculably large number of possible sequences of moves. But we become good at this game of many, many moves not by thinking about more moves but by thinking in terms of larger patterns: patterns of pieces rather than movements by individual pieces.

Through practice, chess masters have trained their mind to recognize the unique meaningful patterns that apply to their game. Further, the ability to learn to recognize new patterns (along with a huge capacity to remember them) seems to be something we all possess, not just chess masters. It is a fundamental principle of learning, at least learning to be a chess expert.

Even more interesting, we don’t recognize this as knowledge, in the sense of things we recognize that we know. I know that I know some things. I know that I know all sorts of facts like the capital of some of the U.S. states and the number of sides in a triangle and Newton’s formula relating force and mass and acceleration. These sorts of things are considered explicit knowledge.[4]

Chess masters can’t write down most of the patterns they know, both because those patterns are so vast and because they use them without thinking about them. The patterns they learn become part of their chess intuition in a manner of speaking. A common technical term for this is tacit knowledge.[5] We use tacit knowledge in our thinking without realizing that we are using it. This is why it took focused research to discover what was going on in the minds of chess masters.

Tacit knowledge becomes part of our perception. Chess masters see the board differently; for example they often immediately see positions as good or bad without having to do the kind of analysis that the rest of us would have to rely upon.[6]

Tacit knowledge is also used automatically in our thinking. When chess masters guess at the best move in a given position, their guess is informed by their vast database of tacit knowledge, so it is very different from the guess made by a weaker player. Experts make better guesses in their area of expertise. This is what I mean by their “chess intuition” above.

Trained Intuition and Better Guesses

You might be wondering at this point why I’ve spent so much time talking about chess experts. Or you may have guessed the answer. The most interesting conclusions from the research on chess masters are by no means limited to chess masters. Very similar or consistent results have been obtained across a staggeringly wide variety of fields from physical pursuits like wrestling and ballet to intellectual subjects like calculus and philosophy to artistic activities like painting and violin playing, to a wide variety of everyday jobs, to oddball activities like picking the winners at the horse races.[7] Even among scientists, where the role of abstract reasoning is particularly central and the subject matter particularly challenging, productivity doesn’t seem to be predicted on the whole by supposed general ability measures such as IQ.[8]

The chess findings are a particularly useful rhetorical device here because chess seems like it should be so dependent on reasoning and analysis. It turns out that experts analyze chess positions with the help of a vast mental database of chess configurations that apply without any recognition that they know them. The resulting perception and memory of the board just seems natural to them as a result of practice. Examined closely, in spite of its natural appearance for some people, the effortlessness of deep expertise seems to be an extreme kind of skill acquisition[9] far more than an expression of talent.

Even if you interpret all of these findings from different fields very conservatively, collectively they still tell us something of tremendous importance about how we become good at things. We modify the way we perceive the activity. In effect, we train our intuition about the activity.

In all of these activities, researchers have found that time spent in the activity lets us acquire a new way of perceiving patterns in that activity that let us transcend the limits of our working memory and sequential reasoning capacity. That’s why expertise consistently outperforms IQ or working memory capacity or other general measures as a predictor of performance in virtually every activity that has been studied so far. And expertise is not just specialized knowledge or skills; it is also more importantly an accumulation of organized tacit knowledge that lets us make better guesses.



[1] (Hunter, 1986)

[2] (de Groot, 1965)

[3] This has been the most common interpretation of the chess research findings amongst expertise researchers, based on the influential theory of Chase and Simon. (Chase & Simon, 1973), (Simon & Chase, 1973)

[4] “Explicit knowledge,” basically just means things we know that can be easily identified and written down. The descriptor declarative is sometimes used as well, meaning that we can declare it.

[5] In contrast to “explicit knowledge,” this is often referred to as “tacit knowledge,” meaning things we know but we can’t easily express, especially things that support action. Tacit knowledge is usually assumed to be useful for doing things more than for taking part in our conscious reasoning processes. The descriptor procedural is sometimes also used for tacit knowledge because we think of it as involving procedures for doing things rather than declarations about things. For this reason, a common rule of thumb is that tacit knowledge refers to “know how” whereas explicit knowledge refers to “know that” (i.e. I know that grass is green). The casual rule of thumb is troublesome because we don’t really know how we do those things we call procedural, the usage of the word “know” in “know how” is very different than the word “know” in “know that.”

[6] Following the pioneering chess research, research into other areas reinforced the same finding: expert performance depends heavily on a large accumulated memory of patterns that give us a different “intuitive perceptual orientation” to tasks. “Experts can ‘see’ what challenges and opportunities a particular situation without affords.” (and without doing any analysis) (Perkins, 1995, p. 82)

[7] One of the leading and best known figures in the study of expertise is K. Anders Ericsson, whose research encompasses a particularly wide range of fields. An excellent and accessible overview of work in diverse areas of expertise research is Ericsson’s edited collection: The Road to Excellence (Ericsson, 1996).

[8] (Taylor, 1975)

[9] (Proctor & Dutta, 1995), (VanLehn, 1996)

Getting the Right Answer: Is it the Right Answer?

What’s the most important thing about problem solving? If you paid attention in school, you probably would respond: getting the right answer!

There’s nothing wrong with wanting to get the right answer. Or is there? I want to raise four related concerns:

1. The problem structuring concern: Problems don’t always arise in a form that has an identifiable single right answer. Often there are different best answers for different sets of possible criteria, with different sets of tradeoffs.



2. The motivated thinking concern: The kind of thinking we do in order to feel we are right, to be seen by others as being right, or to advocate the right answer to others can overwhelm the kind of thinking needed to solve the problem in the best way.



3. The my-side bias concern: We have a natural tendency to look selectively for evidence in favor of the first good guess we make explicit, to ignore evidence for alternatives, and to think in ways that support our favored alternative.



4. The belief overkill concern: The my-side bias is often reinforced in such a way that that certain of our intuitions become treated as aspirations or universal facts of nature and this extends beyond things that can be verified empirically between observers. Compelling intuitions can guide our thinking into limited preferred patterns, reinforced by selective use of evidence and also by social patterns of polarized thinking.

I argue that these concerns, along with various inferences we can reasonably make about how the mind works, necessitates a certain approach to thinking, especially about more difficult problems.

These factors mean that we have to learn to adopt and leverage different perspectives in order to harvest all of the information available and the expertise needed to solve complex problems. This is why when it comes to problem solving worthy of the name, thinking clearly is more important than thinking correctly along predetermined lines.

At this point you probably have your own concerns. You might be wondering whether I am advocating some sort of fluffy relativistic “there’s no right or wrong and all perspectives are valid” sort of approach to thinking.

That’s not the case. I use the term Clear Thinking because I truly believe there is such a thing as identifiably better and worse thinking, leading to better or worse conclusions and that it very often makes a critical difference whether we get it right.

My point is just that all of us (not just other people) assume we are getting it right much more often than we really are getting it right, and that very knowledge about our own thinking processes is a key to Clear Thinking.

This means that when we need to think clearly about complex problems, we need to use our knowledge about and skill at problem solving itself to root out our own shortcuts, make our thinking more explicit, bring alternate perspectives into play, and in general consider more alternatives than would otherwise come to mind.

Clear Thinking: An Introduction

When a difficult problem is solved deliberately, it is generally because the right expertise was applied to good information through the right tools as part of a reliable process. These are the essential elements of effective human problem solving. The rest is in the details. We often do less than this because we are also very good at guessing well.

The human nervous system is not a logic engine, it evolved to serve human biology. This has profound implications for the way we think and what we must do to improve our thinking. Our explanations are guided by powerful intuitions that often seem to defy the theoretical ideal of rationality.

Expertise refers to the way a mind with natural learning abilities organizes its experience purposefully for action. This is where our guessing ability comes from. Expertise provides our built-in guidance for effective thinking in particular areas.

Information is the fuel for thinking, without which expertise would be an engine with a dry tank.

Tools and processes are the way we leverage our strengths and compensate for our weaknesses.

Exceptional problem solvers make better use of available resources than the rest of us and also gather more of the right resources around themselves. This isn’t magic and it isn’t something we’re born with. Nearly anyone can learn to do these things better. Nearly anyone can learn to make better guesses and also to leverage good guesses into more powerful reasoning.

This book introduces an approach which I call Clear Thinking. The basis of this approach is strategic. Through a realistic and accurate ongoing understanding of the strengths and weaknesses of our own mind, we learn to make best use of our ever changing strengths and minimize or compensate for our ever changing weaknesses. In this way we make increasingly better use of our resources and approach the ideal of clear thinking.

You should understand from the start that this is a lifetime learning process. You can’t learn to radically improve your thinking in a weekend seminar, a critical thinking course you can complete in a semester, or even a degree you can earn in a few years. To become smarter you have to learn the mindset, strategies, processes, skills, tactics, and habits of becoming smarter, and this learning is difficult, rewarding, and lifelong.

Clear Thinking is an approach that you incorporate into your daily decision making and problem solving by learning the associated tools and principles and by coming to embody the intellectual virtues shared by the best problem solvers.

Key Points:

--> Useful human knowledge, skills, and attitudes tend to break down into domains. Among other reasons, this is possibly because the human brain is organized into somewhat discrete learning systems for dealing with different kinds of biological needs.

--> Different subjects we learn have their own domain with their own domain-specific rules and methods of study. Our practical abilities tend to be organized into domains for the most part. The domain-specific elements of thinking are critical to Clear Thinking and also to education in general.

--> Most problem solving is relatively routine and involves dealing with particulars of a situation relevant to a specific domain of activity rather than dealing with abstract principles.

--> Since problem solving so often involves dealing with particulars, individual differences in problem solving ability are largely a result of specialized expertise in particular domains rather than a more generalized reasoning ability.

--> Speciallized domain expertise is the result of systematically acquired experience in which we structure our mind in a way that lets us think efficiently about a specific kind of activity in a particular way.

--> We also have important abilities that apply to multiple domains of knowledge at once or which cross domains. These domain-general elements are the ones emphasized when we try to improve problem solving and decision making through “critical thinking” and similar approaches. I have adopted the term Clear Thinking rather than “critical thinking” only because I think the emphasis on criticism can be misleading.

--> Some people are better individual problem solvers than others because they have learned to make use of their cognitive talents, domain-specific expertise, and domain-specific knowledge, by means of domain-general problem solving knowledge, skills, strategies, and attitudes.

--> One of the most critical things we can do in order to improve our thinking is to distinguish domain-specific from domain-general elements. We acquire and apply these different kinds of elements in very different ways and they have different kinds of influence on our thinking.

--> A great significance of domain-general vs. domain-specific elements is partly that more intelligent and more expert problem solvers often make even worse mistakes than less intelligent and less expert problem solvers due to negative artifacts of their abilities such as overconfidence, overspecialization, and the amplification of natural biases.

--> One way we can avoid the worst mistakes is by learning realistically about the strengths and weaknesses of human abilities in general. This becomes an important aspect of our domain-general problem solving knowledge, skills, strategies, and attitudes.

--> The domain-general emphasis of Clear Thinking is mostly intended to make the thinking process more explicit in order to make better use of our guesses. Making the thought process more explicit is the essence of the ideal of rationality.

--> The domain-general elements are also significant because they help us learn how to shift between different perspectives. Importantly, this is not because different perspectives are somehow all equally valid. It is because a perspective is much like a lens which makes some things easier to see than others. Useful bits of knowledge are sometimes obscured by our current perspective, and shifting perspectives can help additional alternatives become more visible.

--> Some groups are better collective problem solvers than others due to differences in the patterns of their interactions in making use of their individual expertise, knowledge, skills, strategies, and attitudes. This becomes another important domain-general element of human thinking.

Does adding men to a group make the group dumber?

Does adding men to a group make the group dumber?

New preliminary finding reported at the Harvard Business Review:

 Adding more women to a group may make the group smarter.

Previous related finding:

 Collective intelligence of groups is roughly independent of intelligence of individual members

Previous assumption:

 A more diverse group is better than a less diverse group.

Surprising new possible implication:

 Gender could potentially be more important than diversity in collective intelligence of a group, with women adding to group intelligence and men detracting from it.

Limitations:

 This is a preliminary finding not yet a robust one. It has been reported in two studies by the same team under a limited range of conditions.

 Collective intelligence is not the only thing of importance in group problem solving.

 The effect of gender may be through process factors that potentially could be achieved in other ways as well if isolated.

 The effect has not been tested very far yet at the extremes.

 Measured of collective intelligence are not as standardized as measures of individual intelligence, which are themselves of mixed value in actual problem solving.

Additional thoughts:

This is a preliminary finding but it seems plausible to me and if it is borne out robustly then combined with the previous finding that collective intelligence is roughly independent of individual intelligence of group members, this seems to mean that (1) group process is more important to collective intelligence than individual insights, and (2) that group process depends strongly on gender.

The first still amazes me but I think it may be true, and if so, the second one seems even more plausible. My impression is that there is a lot less constructive interaction between men than between women in group processes in general.

My own speculation is that while both experience a mixture of task and relationship tension in groups, women leverage the relationship tension more constructively, whereas men tend to align more quickly when they agree and to stonewall more strongly when they disagree. Men usually seem to be more likely to drift toward a goal of satisficing (coming to the first satisfactory solution) and then disengaging, whereas women seem to interact in a more prolonged way. I'm guessing that this contributes to collective intelligence as it is being measured here in some way.

refs:

Article on MindHacks: http://mindhacks.com/2011/06/24/a-dose-of-female-intelligence/

Interview at HBR: http://hbr.org/2011/06/defend-your-research-what-makes-a-team-smarter-more-women/ar/1

Chart (The Female Factor): http://hbr.org/2011/06/defend-your-research-what-makes-a-team-smarter-more-women/sb1

Book Review - Apollo Root Cause Analysis: A New Way of Thinking (?)

Book Review - Apollo Root Cause Analyis: A New Way of Thinking Book by Dean Gano, Apollonian Publications, 2008. Review by Todd I. Stark Link to Amazon review.

Very useful principles for preventing bad things from happening, April 11, 2011 By
Todd I. Stark "Cellular Wetware plus Books"

This review is from: Apollo Root Cause Analysis: A New Way of Thinking (Paperback)

How can we prevent bad things from happening, and how can a formal method help us in this quest? That's the topic of this book.

"By understanding the cause and effect principle and creating a RealityChart, your understanding of what constitutes reality will be changed forever ... allow you to see a reality that was previously beyond your comprehension." (Apollo Root Cause Analysis, p. 3)

"... the problem of a linear language in a nonlinear world while accommodating the simple human mind has been a challenge for the ages. I believe this challenge has been met with Apollo Root Cause Analysis." (ARCA, p. 176)

The importance of the topic covered by this book is immense, so for my purposes, I'll forgive the author his amusing enthusiasm for this own method as seen in the quotes above and try to determine what may actually be truly useful in it rather than dwell on what may or may not be unique about it. I will be referring to this book and to the Apollo method in this review. The method is also implemented with associated RealityCharting(tm) software which is outside the scope of this review, except where explicitly mentioned.

Problem solving well is one of if not the most critical factor for human success across a wide range of activities. One big part of problem solving is explaining *why* something unexpected happened. I you think about it, anytime something unexpected happens, we generally want to know why. We do this naturally, automatically, and effortlessly. We can't stop ourselves from doing it to at least some extent. Sometimes the explanation seems obvious and sometimes it seems very elusive. Even when the explanation seems obvious, there is often more going on of importance than we realized. We do this seemingly because especially if it's a bad thing that happened, understanding why it happened potentially helps us predict whether it will happen again and perhaps prevent it from happening again. That's important to us as individuals as well as to organizations. It's so important that we already do it all the time. We also tend to assume that we're very good at it, I think. My experience is in agreement with the author of this book, people are in general not nearly as good at solving problems as we think we are, at least when the problem become complex and involve multiple people and organizations.

We have a powerful natural ability to make sense of events that happen around us by identifying people, places, things, and weaving a story around how they interact with each other. (1) The power of storytelling to make sense of events is also its tragic downfall when we need to understand how things happen in great detail. Our temptation to tell stories actually gets in the way of understanding what specific things need to happen in order for other things to happen, and that's the sort of understanding that is needed in root cause analysis. Getting past our natural but distracting abilities to use the right tools effectively is the greatest value of an effective formal method. We often look to formal methods to systematize our thinking in general, but problem solving outside the academic realm of worked problems is often not amendable to being constrained by formal methods. Instead, formal methods provide their real benefit in forcing us to look past our own blind spots at key points during problem solving. To his credit, the author of this book seems to have grasped this important point very well and applied it effectively.

The most problematic blind spots in our thinking are those that aren't avoided by intelligence, education, or domain-specific expertise, skills or knowledge. The most problematic blind spots are actually natural abilities that serve us well most of the time. One of the most important examples is that we tend to use storytelling to construct a plausible sequence of events leading to an outcome, putting people into the center of the action and making the events meaningful to us. There are a number of reasons why this natural and compelling process is a bad idea in problem solving.

First, since a story starts with the putative "root cause" and then proceeds to its effects, it tends to assume a single cause. Events don't have a single cause. If our goal is to prevent bad things from happening, we'd rather identify as many relevant causes as possible, and potentially address each one as makes sense.

Second, storytelling tends to center around characters, their motives, and the things they do. The motives and behaviors of people are often the things we have the least reliable control over in problem solving. We'd prefer to find things we can control better if at all possible, and save human choices and our interpretation of human motives as a last resort.

Third, we can usually tell multiple different stories about the same events, depending on what information we emphasize and how we emphasize it, which in turn depends on our motivations and perspective. This means that the choice of which "root cause" we focus on tends to be more political and subjective than a result of careful analysis.

Our storytelling is natural and compelling but it tends to be more to make sense of events than to understand the details of what causes what.

So effective Root Cause Analysis tools and methods should discourage storytelling and instead search the prerequisite conditions and sequences of events that lead to the results we want to prevent. That's the message of this book and its method. So how well does it accomplish this?

Why a formal process?

Causal analysis (or what is sometimes more specifically referred to as "root cause analysis") is the more formal process of doing what we tend to do naturally, try to figure out why events diverged from what we expected. Why do we need a formal process for doing this, if we already do it naturally? Because often we don't do it very well. The very fact that we do it naturally means that we will tend to make *confident guesses* about why things happened. Often more confident than accurate. This far, the author is on solid ground. The author doesn't refer to it explicitly, but there is a very large literature on the cognitive science of decision making that supports the author's claim that people tend to jump confidently to unwarranted conclusions when they first try to explain causation. (2, 3, 6)

One very important reason for a formal process is to systematize the data gathering and analysis process in order to help compensate for our natural biases that lead us to make confident but inaccurate guesses. The value of formal vs. informal processes is debated widely in the decision science literature, since much of our innate intelligence is the result of automatic non-conscious processes that are opaque to us in our own thinking. (5) However in general I think it is a pretty safe conclusion that a good formal process often helps us focus attention on things that we would not otherwise have seen to see past some of our own blind spots. That's the first reasonable rationale for causal analysis methods like Apollo Root Cause Analysis (ARCA) and its primary tool, RealityCharting(tm). Simply having a process to focus us on causes rather than stories is useful in itself.

Why a formal group process?

There are various reasons for using a formal process in organizations. Unexpected events often affect more than one person, often more than one person is part of the problem, and often more than one person has information or perspective needed to figure out what happened. So causal analysis very often becomes a group process. In addition to the reasons for using a formal process in general (to get around our individual blind spots), it is also important in groups in order to help avoid "group blind spots" such as the principles of behavior in groups studied by psychologists. We are often tempted us to go along with the consensus, to protect our own ideas, to react initially negatively to new ideas, and other tendencies that can negatively impact the problem solving process in groups. (6) A good formal group process can help get around our group blind spots just as it can help us get around our individual blind spots. In spite of the well-established problems created by "groupthink" and other group dynamics studied in social psychological, under optimal conditions, groups can often perform significantly better than individuals on some kinds of problems. (7)

The author makes a key point about group processes that he says defies conventional wisdom (p. 9). He implies essentially that the principles of good problem solving are simple and domain-general so should be taught to everyone, whereas domain expertise is deeper and necessarily differs more between people, so "subject matter experts" should also be at hand. The conventional wisdom he says is that problem solving is entirely "inherent to the subject at hand," or what I would call domain-specific, ignoring the value of domain-general principles in problem solving. I don't know how much this really defies conventional wisdom, but I agree with him completely and I think this is an important principle. It is a major part of the rationale for involving more people in a collaborative group problem solving process rather than just pulling in a few experts. If the process is good, and this principle is valid, then it has deep implications for improving problem solving in organizations by broadening involvement and investment in the process itself. That brings us to the real question at hand, whether the author's method accomplishes this objective.

Why Apollo?

So far my description here is I think pretty much in line with the author's rationale. Now we come to the real meat. How good is this particular method at getting around our individual and group blind spots, compared to other methods? That's the significance the author claims for this book and for his method, so that's what we really need to know to evaluate this book. The book covers a lot of ideas that are very important to know, but many are common knowledge to experienced problem solvers, so I am not going to focus on those. What I will focus on is what is supposed to be special about ARCA and RealityCharting(tm) in particular.

According to the author, the key distinction between his formal method and all others is that all others are ways of categorizing causes and schemes for voting on the best way to categorize them, while his method discerns (and RealityCharting displays) the actual relationship between different causes, along with the evidence for each cause. (p. 193)

All viable methods of "root cause analysis" in groups involve taking chains of events that would be too numerous and related in too complex a manner to envision in a common way by everyone involved in the process of they were simply described in words. Visual representations of trees of causes play a key role in all formal methods of "root cause analysis," including ARCA.

The trick to understanding the underlying message of this book and grasping the uniqueness claimed for the method is to understand exactly what he means by the *relationships between causes* and how the method forces you to think about evidence for causes. I don't personally favor the author's explanation for why his method is unique. His rules of causality seem awkward to me and I think there is a slightly different and clearer way to think of how his method works.

First, every effect is also a cause. There is no distinction between causes and effects except at the endpoints. Feedback loops of causes that have effects that ultimately lead back to the original cause are common in nature as well as human design and this method has no problem handling them. We start with the effect we are trying to explain and end with causes that either do not need to be explained or cannot yet be explained. This seems fairly straightforward to me for a causal chain, I'm not sure why the author feels it is unique to his method. I suppose it may be that commonly used methods tend to de-emphasize this aspect of causality.

Second, the method forces you to identify both actions and conditions that had to be in place for the action to cause the effect. This isn't useful for causal modeling as much as it is a useful trick for helping to identify all of the causes that might be relevant to solving the problem. Breaking the "Why?" question into an action and a set of conditions is seemingly somewhat unique to this method and helps avoid thinking solely in terms of actions or solely in terms of things that were in place at the time or characteristics of the situation. The theory is that it is usually *easier* to see actions that happened and less obvious what conditions had to be in place as well, but it is often *more useful* to identify the conditions because they tend to be more predictable and more controllable.

Third, the method forces you to have evidence for each cause, and takes an unusual and perhaps mildly questionable empiricist slant here. It distinguishes "sensed" from "inferred" evidence. "Sensed" of course refers to direct observation by someone, with as little interpretation as possible. "Inferred" refers to anything else. Assumptions and opinions represent doubts and must be investigated further, and just about anything that someone doesn't observe directly themselves has some doubt associated with it, whereas direct observation is automatically given very high credibility by the rules of the methodology.

This is a slightly odd sort of slant from my perspective because the credibility of eyewitness testimony in general is often in doubt (8), the reliability of inference is often not obvious, and because in my experience, hypothesis testing is particularly useful and important problem solving tool. Storytelling, much maligned and minimized in the ARCA method for good reason, is built on inferences from observations. However so do scientific theories and mathematical models arise from inferences. (9) The main difference is that science and math involve systematic linking and pragmatic testing of inferences rather than just weaving meaningful narratives. The author frequently asserts in different ways that storytelling negatively impacts problem solving, and defers to direct observation. I would instead argue that both are in doubt, and that their relative strength is not absolute based on source, but depends upon how inferences and observations support each other. One contemporary philosopher usefully compares the relationship to that of a crossword puzzle. (10) It is notable that the ARCA method and reality charting tool have no trouble accommodating different conceptions of the reliability of evidence.

Strengths

1. The philosophy behind the method is extremely inclusive organizationally. The idea is not to find the putative "true causes" but to gather enough information to produce effective solutions. So the method encourages as many people as possible to participate as fully as possible, rather than to argue over the right causes to focus on. That is perhaps the greatest strength of this method, its formal incorporation of diverse perspectives, allowing everyone to be heard and every perspective considered while discouraging the usual drama of competing narratives. Different definitions of the problem are managed by starting from different primary causes and creating separate charts. Since the framing of the problem often guides or constrains the search for a solution, I consider this a strength of this method.

2. ARCA explicitly includes the source and specifics of evidence for each cause, helping to identify and prioritize information gathering tasks to further validate assumptions and opinions, or reproduce observations. You are strongly encouraged to provide evidence for every cause, a process that I think can lead to a far more comprehensive yet focused data gathering effort than other methods.

3. ARCA allows for the explicit representation of causal feedback loops, which can be important factors in understanding what is happening. Many causal charts make this important relationship very difficult.

4. The technique of treating each effect as a cause and subjecting each to requirements for evidence, and explicit follow-up for finding further causes, intermediate causes, information required, or explicit reasons for stopping is a very powerful aid to causal thinking and to me is the core of both the method and the associated software.

Limitations and Criticisms

1. Potential overreliance on unreliable eyewitness testimony vs. discouragement of useful inferences. The process of hypothesis generation is a particular kind of inference where we consider more alternatives than we suggest, and as a result we understand that they are explicitly hypotheses. There is experimental evidence that generating hypotheses ourselves leads to a more realistic appraisal of their strength and less false confidence than inferences provided from other people. (cf. Derek Koehler) Other research supports the notion that our causal judgments are themselves inferences that often result from mental simulation. [This is a comment regarding the method in the book, which downplays the role of "inference" vs. "direct observation." It is a minor point in practice because there is nothing in the RealityCharting tool that forces you to evaluate sources of evidence in a particular way.]

2. Overemphasis on uniqueness of the method. This mostly refers to the tone in which the book is written, which I often found distracting and annoying, wherein it often makes the book sound like a sales presentation for the method and the tool rather than a treatise on effective problem solving. This point goes along with the scarcity of credit to other sources that for me would have greatly enriched my appreciation of the rationale for the method. The author relies too heavily on himself as an authority for his rationale to rate this a five star book.

3. Restriction of causal statements to very brief verb-noun and noun-verb format rather than sentences. I think this has some value in fostering clarity if you manage to find a phrase that everyone happens to understand in the same way. Still, in practice I found that it can be time consuming to come up with these compact ways of expressing causes, and that they were easier to misinterpret than full sentences would be. [This is also a potential issue in the tool because the rule checking tries to enforce it. However, it can be disabled if desired.]

4. Does not allow you to visibly categorize causes except into actions vs. conditions. This is an attribute of both the method and the associated software. This "limitation" is the intentional result of the author's core principle that he feels distinguishes his method in particular: categorizing causes is relatively useless compared to relating them. Categorizing causes is suggested in ARCA only if you are stuck finding plausible candidate causes and want to try thinking in terms of categories in order to help find candidates. Since this is intentional, it is a limitation only in the sense that someone familiar with other methods may run into it and have to think differently in order to use the method as intended.

Given that so far every substantive criticism I've had of this method is easily accommodated by minor changes to the method (as well as in the associated tool, with the exception of categorizing causes) I found this to be a particularly versatile method. It captures a number of key principles of problem solving, especially in groups, and provides practical and effective ways of compensating for our most problematic blind spots and biases. For the most part, I think understanding the principles of ARCA and RealityCharting would probably enhance most other methods as well as the method standing on its own.

A formal method itself is no substitute for improving the reflective intelligence of each problem solver, but I think this method could go a long way in any organization toward getting people to think more effectively, and especially for helping them communicate their best thinking to each other. I found very little to disagree with and much of value in this book and found the method easy to understand and apply.

References

(1) Brian Boyd on art and storytelling as biological adaptations which derive from play.

"On the Origin of Stories: Evolution, Cognition, and Fiction," Brian Boyd, 2009, Belknap Press of Harvard University Press

(2) David Perkins discusses the significance of domain-general principles for effective thinking, and why they can't be replaced completely by intelligence or expertise. A plausible research-based discussion of why we need help getting around our built-in individual blind spots.

"Outsmarting IQ: The Emerging Science of Learnable Intelligence," David Perkins, 1995, Simon and Schuster

(3) The psychological study of how we tend to answer questions of the form "Why ... ?" is called attribution theory. Probably a result of our bias toward storytelling explanations, most attribution theory has attempted to address attributions of people in social situations and sometimes products in the case of consumer research. A much smaller amount of research has been done on the perception of causality in other ways, such as in building causal explanations, and only a tiny amount has made its way into popularly accessible books.

"Causal attribution: from cognitive processes to collective beliefs," Miles Hewstone , Wiley-Blackwell

(4) There are not many modern books that deal relatively broadly with causal modeling from both a philosophical and mathematical perspective. One of the few is Pearl's text.

"Causality: Models, reasoning, and inference," J. Pearl, 2000, Cambridge University Press

(5) The non-conscious processes in realistic problem solving is addressed by a fairly sizeable body of technical literature by Dijksterhuis, Bargh, Gollwitzer, and others, and introduced in an accessible and practical way by Gary Klein.

"Sources of Power: How People Make Decisions," 1999, Gary Klein, MIT Press

(6) The problems exacerbated by group behavior under non-optimal conditions are introduced accessibly and briefly yet very broadly in:

"The Psychology of Judgment and Decision Making," Scott Plous, 1993, Mcgraw-Hill.

(7) The positive potential of groups under optimal conditions is discussed in
"Group Problem Solving" by Patrick Laughlin, 2011, Princeton University Press.

The common belief that direct observation is inherently more reliable than inference is based on two ideas that are at best only partially true: (1) the assumption that reports based on observation does not significantly depend on memory, inference, or explanation, and so is automatically free of distortions, biases, or storytelling, and (2) the assumption that inferences are roughly equivalently reliable under a wide range of conditions.

(8) On the reconstructive nature of memory for events, see:
"Searching for Memory: The Brain, The Mind, and the Past," Daniel L. Schacter (1997), Basic Books

(9) On the nature of inference and especially scientific inference, see:

"Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science," Mayo and Spanos (2010), Cambridge University Press

(10) The useful crossword puzzle metaphor for the relationship of observation and inference is introduced in:

"Evidence and Inquiry: A Pragmatist Reconstruction of Epistemology," Susan Haack (2009), Prometheus Books

A lifetime learning approach, more than a course in "critical thinking"

Review of --> Critical Thinking: Learn the Tools the Best Thinkers Use

by Richard Paul

A lifetime learning approach, more than a course in "critical thinking."

I enjoyed this book and got a lot out of it. For such a high level book it isn't easy reading because it requires you to think about your own thinking while you are reading it in order to get full value out of it. If you skip that exercise, in my opinion, you will not appreciate the value of this book. So if you want to grow from what the authors are offering, be prepared to take a long term vision of your own abilities and to do a lot of work. In spite of the somewhat misleading title of this book, it is not really about what most people would call "critical thinking," it is a much deeper and more useful view of thinking abilities in general.

This book presents a relatively accurate (in my opinion) and very usable model of how human reasoning works in practice and how we get better at it. In addition it offers many simple but practical drills for identifying how well you are thinking and what sorts of things would be good to work on. So I do recommend this book.

Having gotten that positive impression from my reading, I also noticed the many very negative reviews here and that made me pause and think a bit. It may be helpful to expand on what I think it good about this book and what the negative reviewers are seeing.

(...)

Link to complete review on Amazon

Book Review: David Perkins' must-read brilliant map of human thinking ability and its improvement

Review of David Perkins’ “Outsmarting IQ: The Emerging Science of Learnable Intelligence,” 1995, Free Press.

Link to review on Amazon --> http://www.amazon.com/review/R3AYGZV7G7AUTO/ref=cm_cr_rdp_perm

Profound Thinking By Example

This is the single best book I’ve come across on the potential for improving human thinking ability. I give it my highest recommendation; I think it should be read by everyone interested in problem solving, decision making, and human abilities in general. It is amazingly broad in its coverage of data, profoundly deep in its treatment of specific lines of relevant evidence, and ingenious in its vision of the future.

What impressed me most about this book is that the author, David Perkins, demonstrates the power of deep reflective thinking by his own example in the organization and treatment of evidence throughout this book, in his critical treatment of his own evidence and ideas, in his creative original ideas, and in his effective consolidation and filtering of massive amounts of research. Showing how asking the right questions can help us understand seemingly contradictory data about intelligence, Perkins gives an engaging plausibility proof for the kind of reflective intelligence he argues for in this book.

The Concept of Realms of Thinking

To give away the ending, the book culminates in a model of problem solving ability based on the metaphor of a map. Human thinking ability results from learning our way around. Navigation is fundamental to all sorts of human thinking. Perkins suggests that all intelligent human thinking results from navigation of various kinds, which can be thought of in terms of levels of realms. Perkins organizes the realms in an overall map or “mindscape” from the lowest level of specific contexts of thinking to the highest level dealing with thinking itself.

In learning to solve problems we not only learn our way around physical realms geographically, but we learn our way around specific contexts we find ourselves in such as the realm of buying a house or the realm of choosing a career. We learn our way around different situations like resolving conflicts or making purchases in general. We learn our way around professional fields like law, physics, and mathematics, and areas of technical expertise such as probability and statistics, game theory, and business. We learn our way around the use of tools. We learn our way around various basic kinds of challenges like problem solving, decision making, planning, and learning. Finally, at Perkins’ top level, which he calls thinking dispositions, and we learn our way around thinking itself in terms of the qualities and attitudes that make it more or less effective.

Perhaps the central thrust of this book is that in organizing human problem solving areas into navigational realms, Perkins is not just providing a training map for learning problem solving skills a million different areas, he is also making a case for the learning the critical skills of navigation itself.

Perkins’ realms are very similar to the traditional concept of domains of expertise, but different in one critically important way: realms emphasize the central skills of navigation rather than just the use of repetition or rote memorization or even just the use of deliberate practice. The concept of realms makes it more explicit that all areas of ability that we learn share some commonality in terms of key skills and attitudes we need for navigation itself.

It is learning to be a better navigator; in all realms of human thinking and not just certain subset of them; that is the central message of Perkins’ book. This is encapsulated in his concept of “reflective intelligence.” Reflective intelligence is the aspect of intelligence that can be most improved for the greatest effect across the range of all realms of thinking. Perkins reviews a number of different attempts to improve human thinking and makes various suggestions based on their results regarding specific kinds of changes that can be made to educational curricula in order to teach children to be better navigators in all areas.

Getting Perspective on Intelligence through 3 Dimensions

In giving away Perkins’ final model, I’ve skipped over two very important and interesting aspects: his argument for the model he uses and for the prospect of learnable intelligence through better navigation, and his predictions for important areas of the evolution of learnable intelligence.

The bulk of Outsmarting Intelligence deals tightly with the subject of the title, the legacy of how intelligence has been envisioned and researched so far. Perkins deals in equally deep, reflective, careful, and often fascinating manner with: (1) the evidence for a single common problem solving ability from psychometric data, (2) the evidence showing us how novices think differently from experts, and (3) the evidence showing us what happens when we try to learn general skills and rules for solving problems in general and how computers solve problems.

From these three bodies of evidence, Perkins derives three corresponding dimensions of human intelligence: (1) a neural intelligence dimension which respects what psychometric data gets right and is most closely associated with what we typically assume IQ tests are measuring, (2) an experiential intelligence dimension which respects what expertise research data gets right, and (3) a reflective intelligence dimension which respects what we have learned about metacognition and from the various programs that have tried to teach thinking skills in general.

Neural intelligence, Perkins concludes, is a real dimension of human ability and very important in some situations especially, but it is simply the wrong target for attempts at improvement for various reasons.

Experiential intelligence represents most of our actual problem solving abilities in practice.
Faced with novel and complex situations where we have no relevant experience, our neural intelligence gives us our best chance at solving the challenges presented. But once we have been acquiring experience in an area, a difference in expertise will make people better problem solvers in that area than will a difference in general intelligence.

So experiential intelligence and neural intelligence work together to make us the generally good problem solvers that we are in most situations: neural intelligence helps us deal with novelty and complexity, and experiential intelligence helps us acquire the knowledge and skills we need to deal with specific domains.

So the obvious question is: what role does reflective intelligence play and why does Perkins consider it so important?

The Significance of Reflective Intelligence

Perkins reviews various lines of research into the wide variety of situations where otherwise powerful problem solving abilities seem to fail us in systematic ways. He looks at social psychological effects, cognitive shortcuts, and so on, similar to other reviews of blind spots in human thinking by many other authors except that Perkins attempts to characterize these foibles specifically in terms of side effects of our experiential intelligence.

Perkins suggests that the human mind is mostly akin to a pattern seeker and pattern-driven problem solving engine and as a result its weaknesses are also those we would expect from a pattern-driven process. The human mind often tends to be hasty, narrow, fuzzy, and sprawling.
HASTY. The goal of a pattern seeking intelligence is to find the right response that most closely matches the current situation rather than making an exhaustive search. As a result, our experiential intelligence tends to mislead us to jump to hasty conclusions when the situation is an unusual variation of a known situation.

NARROW. As a result of efficiently seeking patterns we have already seen, the domain-specificity of expertise tends to make us think in narrow ways when we think we have grasped the situation rather than to broaden our thinking.

FUZZY. Part of the power of pattern-matching is that we can so often generalize the lessons from one situation to another similar one. In situations where the appearance is very similar but the underlying principles are different, again our pattern matching effectiveness leads to mistakes: we overgenerallize from our experience.

SPRAWLING. When a pattern-seeking process does not have a single clear path to follow, as often happens in very complex situations, it will tend to follow one path after another and keep switching back and forth rather than working toward an overall goal.

Experiential intelligence, Perkins concludes, is an elegant system for long-term moderate success. When situations are new to us or complex, we get help from our neural intelligence and we have also learned various tricks for getting around our weaknesses, and these are largely accounted for in reflective intelligence. Reflective intelligence represents realms where we think about our own thinking in order to avoid settling on hasty conclusions, to broaden our thinking beyond the initial scope we assumed, to use precision to distinguish similar looking but different things, and to stay on track when notice we are sprawling.

This explains why reflective intelligence is so important to us in tricky situations where we have inadequate experience and where experience misleads us. But it also helps explain, in Perkins’ view, why reflective intelligence is so important for us to learn to be better thinkers in general. Neural intelligence does not replace experiential intelligence, it tends to reinforce it.

When we don’t have experience, neural intelligence helps us grasp the situation, but when we do have experience, we tend to use our neural intelligence to reinforce what our experience already tells us. That’s one big reason why genius is not simply high IQ. That’s why reflective intelligence is so important, it is the tool we use to remind us of the weak points in our own thinking and help us compensate for them regardless of our experience and general intelligence. The abilities and traits we need in order to overcome our blind spots are learnable. A large and crucial aspect of intelligence is learnable.

Existing Approaches: How they Compare

There are various approaches to teaching reflective intelligence, and Perkins reviews the best known and the best studied among them such as Project Intelligence, Reuven Feuerstein’s Instrumental Enrichment, Edward de Bono’s CORT, and Matthew Lipman’s Philosophy for Children, and others, reviewing their approaches and their results and comparing and contrasting them in order to get a sense of what it takes to enhance reflective intelligence.

One of the things that distinguishes Perkins as a deep reflective thinker himself is that he anticipates, researches, and deals fairly with opposition to his arguments. The very idea of learnable intelligence has in the past come under attack from several angles such as past failures of various programs which tried to teach improved thinking, the implications of expertise and psychometric research data, the apparent weakness of general methods for problem solving, and the challenge of transfer between learning domains. Perkins addresses each of these concerns in turn, resulting in a very persuasive case for the very real improvability of intelligence through changes in education.

The Future of Learnable Intelligence

Toward the end of the book, Perkins reveals the ingenuity of his vision through his discussion of several areas for the future evolution of reflective intelligence: areas which ended up being (remarkable for a book written in 1995) accurate predictions of areas that have since become central areas of interest for science and human improvement in general:

1. Intelligence can become distributed -- good thinking depends upon artifacts to offload the limitations of our attention and memory, and we can use our symbol systems and tools to help us keep track of things we could not track individually. This is a wonderful general description of how we are attempting to use computer networks to help us manage complexity (as opposed to some of the more superficial books in recent years which imply that networks somehow replace rather than enhance individual thinking).

2. Intelligence can embrace complexity -- through information visualization tools, effective use of classification, tagging, and finding things by meaning, consolidation, filtering, the mathematical tools for finding large scale patterns in complex phenomena, and by eliminating narrow information silos, we can use our intelligence to solve increasingly complex problems.

3. Intelligence can be dialectical -- this means raising the level of thinking from lower level more concrete concerns to higher order patterns by recognizing the properties specific to complex systems. Perkins offers Peter Senge’s “The Fifth Discipline” and Murray Gell-Mann’s “The Quark and the Jaguar” as exemplifying ways of understanding dialectical intelligence.

Perkins covers a massive amount of data about intelligence and problem solving, summarizes it effectively, and applies it to a practical, powerfully supported, and exceptionally understandable approach to improving human life by teaching ourselves to be more intelligent. Thinking well in general is an unnatural act but we can learn to do it. All that is left is for us to overcome the ideological and political barriers. This book would make a wonderful, gentle manifesto for that grand effort.